Software Engineer Enablement and Product

Job description

kausable is moving from a pure research lab toward products used by real customers. As our software engineer for enablement and product, you own two scopes. Enablement : internal platforms and tools, developer workflows; in a sense products where the customers are your research-science colleagues. Product : externally shipped functionality. This is the software layer around our AI: the applications through which users interact with our AI and the delivery infrastructure that unlocks them. The boundary with our ML Product Engineer is deliberate. That role owns model serving, evaluation and inference. You own the adaptable software systems through which researchers build and users experience those capabilities. Tasks Build customer-facing applications and integrations from scoping through delivery. Build internal platforms and tools that reduce setup time and cognitive load for researchers. Improve researcher experience through reproducible environments, CI/CD, automation and clear repository standards. Design APIs, workflows and frontend interfaces that keep complex model capabilities usable. Contain technical debt through thoughtful abstractions, testing and documentation. Act as a technical bridge between researchers, product stakeholders and the people using what we build. Requirements Broad software engineering experience across backend, infrastructure and at least one modern frontend stack. Deep understanding of software design and complexity management: you build systems that remain easy to change. Strong Python skills plus TypeScript, JavaScript or another modern frontend language. Experience with containers, cloud infrastructure and production delivery. A track record of customer-facing or cross-functional project work. Clear communication, strong problem solving and a pragmatic, self-directed operating style. We are primarily hiring at senior level. We are also open to exceptional candidates with fewer years of experience who can demonstrate comparable depth, judgment and ownership. Nice to have: Internal developer platforms, research tooling or ML infrastructure. Experiment tracking, data tooling or reproducible compute environments. C++ or Rust for performance-sensitive services and tooling. Enough exposure to ML systems to collaborate productively with researchers without being expected to build models. Prior startup, consulting or solutions-engi…